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Proceedings ArticleDOI

Free-text user authentication technique through Keystroke Dynamics

TL;DR: This paper is considering rhythm not only the entered common words or some sequence of common characters, here machine intelligence relies on the fact that it stores the typing style of some daily used words which are supported by the user and can be used as a secret key.
Abstract: Some common words (name, address, E-mail ID, …), we press daily and we are habituated to press it in same rhythm, which is unique and can be used to segregate and distinguish people. In this paper we are considering rhythm not only the entered common words or some sequence of common characters. Here machine intelligence relies on the fact that it stores the typing style of some daily used words which are supported by the user and can be used as a secret key. Recognising typing style promises a parameter like behavioural biometric characteristics that may facilitate non-intrusive, cost-effective and continuous monitoring. But this technique, as of now, suffers from accuracy level and performance. In order to realize, this technique in practice a higher level of security and performance together with low cost version is needed with an error to an accepted level. Hence, it is highly essential to identify the controlling parameters and optimise the accuracy and performance as well as cost with new algorithms.
Citations
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Proceedings ArticleDOI
01 Nov 2019
TL;DR: A machine learning approach is proposed to develop an authentication system that provides more robust user identity information using keystroke dynamics biometrics and shows the increased reliability of the system for authentication purpose.
Abstract: Authentication systems have laid the foundation for validating and securing user's identity. Due to increasing vulnerabilities, the traditional methods like passwords, PINs, tokens etc. cannot keep up with the challenges. The behavioural biometrics like Keystroke dynamics is used to authenticate a legitimate user through typing patterns. In this paper, we propose a machine learning approach to develop an authentication system that provides more robust user identity information using keystroke dynamics biometrics. One-v/s-all classification is applied to 51 users of CMU dataset. For optimization, the SVM classifier is used along with Grid Search for parameter tuning. Grid Search satisfies the goal of choosing the best kernel parameter pair. Experimental evaluation in a heterogeneous environment yields a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 10.24%. The graphical representation of the result is expressed through ROC (Receiver Operating Characteristic) curve which shows the increased reliability of the system for authentication purpose.

5 citations


Cites methods from "Free-text user authentication techn..."

  • ...The stored timing information is then used to build features like down-down key latency, hold time, up-up key latency, down-up key latency, up-up key latency, up-down key latency [21]....

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Book ChapterDOI
01 Jan 2019
TL;DR: The performance of machine learning methods are changed significantly in changing dataset in keystroke dynamics domain, but the evaluation performance of FRNN-VQRS in the experiment is promising and consistent in identifying traits.
Abstract: As of now, the performance of keystroke dynamics biometric in user recognition is not acceptable in practice due to intra-class variations, high failure to enroll rate (FER) or various troubles in data acquisition methods or diverse use of sensing devices. As per the previous study, the performance of this technique can be improved by incorporation of gender information, a soft biometric characteristic, extracted from the typing pattern on a computer keyboard that provides some additional information about the user. This soft biometric trait has low user discriminating power but can be used to enhance the performance of user recognition in accuracy and time efficiency. Furthermore, it has been observed that the age group (18–30/30+ or <18/18+), gender (male/female), handedness (left-handed/right-handed), hand(s) used (one hand/both hands), typing skill (touch/others), and emotional states (anger/excitation) can be extracted from the way of typing on a computer keyboard for single predefined text. In this paper, we are interested in identifying multiple soft biometric traits using two leading machine learning methods: support vector machine with radial basis function (SVM-RBF) and fuzzy-rough nearest neighbor with vaguely quantified rough set (FRNN-VQRS) on multiple publicly available authentic and recognized keystroke dynamics datasets collected through a computer keyboard as well as touchscreen phone. The performance of machine learning methods are changed significantly in changing dataset in keystroke dynamics domain, but the evaluation performance of FRNN-VQRS in our experiment is promising and consistent in identifying traits. At the end, the impacts of the incorporation of soft biometric traits with primary biometric characteristics in user recognition are presented and compared the evaluation performance of nine anomaly detectors.

4 citations

References
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Journal ArticleDOI
TL;DR: This paper examines an emerging non-static biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type in an effort to confront the new threats unveiled by the networking revolution.

772 citations


"Free-text user authentication techn..." refers background in this paper

  • ...F. Monrose et al. [7] proposed keystroke dynamic as a biometric for authentication in 2000....

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Journal ArticleDOI
TL;DR: A method of verifying the identity of a user based on a stream of latency periods between keystrokes, and results from trial usage of the system are reported.
Abstract: The variables that help make a handwritten signature a unique human identifier also provide a unique digital signature in the form of a stream of latency periods between keystrokes. This article describes a method of verifying the identity of a user based on such a digital signature, and reports results from trial usage of the system.

545 citations


"Free-text user authentication techn..." refers methods in this paper

  • ...…typing pattern of entering common words or sequence of common characters and then system calculates the duration of the depressed characters (dwell time) and pause duration between each subsequent characters (flight time) entered, which is defmed by Joyce, Monrose and Roy et al [5] [6] [7] [8] [9]....

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01 Jan 1980
TL;DR: The authors found that of the large number of digraphs represented in most ordinary paragraphs, there were five which could serve as a basis for distinguishing among the subjects, and that this method of distinguishing subjects might provide the basis for a computer authentication system.
Abstract: : The growing use of computers to store sensitive, private, and classified information makes it increasingly important to be able to determine with a very high degree of confidence the identity of an individual seeking access to the computer. This report summarizes preliminary efforts to establish whether an individual can be identified by the statistical characteristics of his or her typing. Can people be identified by the way they type? To investigate this question, an experiment was carried out at Rand, in which seven professional typists were each given a paragraph of prose to type, and the times between successive keystrokes were recorded. This procedure was repeated 4 months later with the same typists and the same paragraph of prose. By examining the probability distributions of the times each typist required to type certain pairs of successively typed letters (digraphs), the authors found that of the large number of digraphs represented in most ordinary paragraphs, there were five which, considered together, could serve as a basis for distinguishing among the subjects. The implications of this finding are that touch typists appear to have a typing "signature," and that this method of distinguishing subjects might provide the basis for a computer authentication system.

389 citations


"Free-text user authentication techn..." refers background in this paper

  • ...Gaines et al. in 1980 produced an extensive report of their investigation with seven typists into keystroke dynamics [3]....

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Journal ArticleDOI
TL;DR: By performing real-time measurements of the time durations between the keystrokes when a password is entered and using pattern-recognition algorithms, three online recognition systems were devised and tested.
Abstract: An approach to securing access to computer systems is described. By performing real-time measurements of the time durations between the keystrokes when a password is entered and using pattern-recognition algorithms, three online recognition systems were devised and tested. Two types of passwords were considered: phrases and individual names. A fixed phrase was used in the identification system. Individual names were used as a password in the verification system and in the overall recognition system. All three systems were tested and evaluated. The identification system used 10 volunteers and gave an indecision error of 1.2%. The verification system used 26 volunteers and gave an error of 8.1% in rejecting valid users and an error of 2.8% in accepting invalid users. The overall recognition system used 32 volunteers and gave an error of 3.1% in rejecting valid users and an error of 0.5% in accepting invalid users. >

328 citations


"Free-text user authentication techn..." refers methods in this paper

  • ...After then S. B1eha submitted his PhD thesis on Recognition system based on keystroke dynamics in 1988 [4]....

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Journal ArticleDOI
TL;DR: An insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades is provided, as well as offering suggestions and possible future research directions.
Abstract: Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up-to-date extensive survey and evaluation is not yet available. The objective of this paper is to provide an insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades, as well as offering suggestions and possible future research directions.

289 citations


"Free-text user authentication techn..." refers background in this paper

  • ...It updates itself continuously by Growing Window, Moving Window or Adaptive threshold mechanism, defined by Pin, S. T. and Giot R. et.al [12] [13] [16], which can help to recover the account and minimise Equal Error Rate (EER) in future....

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